most citedContextual Agentic Memory is a Memo, Not True Memory

1 citations · 1 across the 11 of their papers we have counts for

collaborators

11 papers

cs.DL2026

Reviewer Scores Are Not Comparable Across Research Areas in ML Peer Review

Binyan Xu, Xilin Dai, Fan Yang +1

Peer review at ML conferences increasingly relies on reviewer scores as the primary decision instrument. As submissions have scaled from thousands to tens of thousands per year, no…

cs.CL2026

LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via State Proprioception

Binyan Xu, Haitao Li, Kehuan Zhang

Long-horizon tool agents are bottlenecked by how their context grows toward the limits of the context window. Recent systems make context management agent- or system-controlled, bu…

cs.AI2026

When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting

Binyan Xu, Xilin Dai, Fan Yang +1

AI agents can now take irreversible actions in operational systems, but agent-caused losses are still not clearly assigned, priced, or transferred. Providers often disclaim consequ…

cs.LG2026

Trapping Attacker in Dilemma: Examining Internal Correlations and External Influences of Trigger for Defending GNN Backdoors

Fan Yang, Binyan Xu, Di Tang +1

GNNs have become a standard tool for learning on relational data, yet they remain highly vulnerable to backdoor attacks. Prior defenses often depend on inspecting specific subgraph…

cs.CR2026

Beyond Nodes vs. Edges: A Multi-View Fusion Framework for Provenance-Based Intrusion Detection

Fan Yang, Binyan Xu, Di Tang +1

Provenance-based intrusion detection has emerged as a promising approach for analyzing complex attack behaviors through system-level provenance graphs. However, existing defense me…

cs.AI2026★ 1 cited

Contextual Agentic Memory is a Memo, Not True Memory

Binyan Xu, Xilin Dai, Kehuan Zhang

Current agentic memory systems (vector stores, retrieval-augmented generation, scratchpads, and context-window management) do not implement memory: they implement lookup. We argue…

cs.AI2026

From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?

Binyan Xu, Dong Fang, Haitao Li +1

Multi-agent systems (MAS) for structured data-science tasks externalize analytical control through workflows spanning stages, tools, shared state, verification, and repair. Distill…

cs.LG2026

From Internal Diagnosis to External Auditing: A VLM-Driven Paradigm for Data-Free Online Backdoor Defense

Binyan Xu, Fan Yang, Xilin Dai +2

Deep Neural Networks remain inherently vulnerable to backdoor attacks. Traditional test-time defenses largely operate under the paradigm of internal diagnosis methods like model re…

cs.CV2025

Breaking the Stealth-Potency Trade-off in Clean-Image Backdoors with Generative Trigger Optimization

Binyan Xu, Fan Yang, Di Tang +2

Clean-image backdoor attacks, which use only label manipulation in training datasets to compromise deep neural networks, pose a significant threat to security-critical applications…

cs.MM2025

CLIP-Guided Backdoor Defense through Entropy-Based Poisoned Dataset Separation

Binyan Xu, Fan Yang, Xilin Dai +2

Deep Neural Networks (DNNs) are susceptible to backdoor attacks, where adversaries poison training data to implant backdoor into the victim model. Current backdoor defenses on pois…

cs.CR2025

One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP

Binyan Xu, Xilin Dai, Di Tang +1

Deep Neural Networks (DNNs) have achieved widespread success yet remain prone to adversarial attacks. Typically, such attacks either involve frequent queries to the target model or…